Medical image diagnosis assistance apparatus and method using plurality of medical image diagnosis algorithms for endoscopic images

ABSTRACT

Disclosed are an apparatus and method for assisting the diagnosis of a medical image by an automated system. A computing system includes a memory or database that stores a plurality of medical image diagnosis algorithms each having a medical image diagnosis function. A processor inside the computing system extracts diagnosis requirements for a medical image by analyzing the medical image, selects a plurality of diagnosis application algorithms to be applied to the diagnose of the medical image from among a plurality of medical image diagnosis algorithms based on the diagnosis requirements, and generates display information including diagnosis results for the image frame by applying the plurality of selected diagnosis application algorithms to the image frame.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims under 35 U.S.C. § 119(a) the benefit of priorityto Korean Patent Application No. 10-2019-0139885 filed on Nov. 5, 2019,which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present invention relates to an apparatus and method for assistingthe diagnosis of a medical image by an automated system. Morespecifically, the present invention relates to a method for generatingevaluation scores for artificial intelligence-based medical imagediagnosis algorithms and assisting the diagnosis of a medical imagebased on the evaluation scores and an apparatus for performing themethod.

BACKGROUND ART

Endoscopic diagnosis is a medical practice that is considerablyfrequently performed for the purpose of regular medical examination.There is a demand for a technology that processes real-time images uponendoscopic diagnosis to preprocess them so that an expert can easilyidentify a lesion at a medical site. Recently, U.S. Patent ApplicationPublication No. US 2018/0253839 entitled “A System and Method forDetection of Suspicious Tissue Regions in an Endoscopic Procedure”introduced a technology that performed a preprocessing process ofremoving noise from an image frame(video frame) and performed a noiseremoval preprocessing process and a computer-aided diagnosis (CAD)process in parallel, thereby providing real-time diagnosis assistancedisplay information.

In this technology, the accuracy and reliability of a CAD module arerecognized as significantly important factors.

Technologies for segmenting or detecting objects in an image orclassifying objects in an image are used for various purposes in imageprocessing. In a medical image, objects in the image are segmented,detected, and classified based on the brightness or intensity values ofthe image, in which case each of the objects may be an organ of thehuman body, or a lesion.

Recently, the introduction of deep learning and a convolutional neuralnetwork (CNN) as artificial neural networks into the automation of animage processing process has dramatically improved the performance of anautomated image processing process.

However, on the other hand, the insides of recent artificial neuralnetworks, such as deep learning and a CNN, approximate black boxes, andthus there is reluctance for a user to fully accept and adopt them evenwhen acquired results are excellent. In particular, reluctance toartificial neural networks stands out as being more important in themedical imaging field in which human life is dealt with.

Under this background, research into explainable artificial intelligence(X-AI) has been attempted in the Defense Advanced Research and Planning(DARPA) of the U.S., etc. (seehttps://www.darpa.mil/program/explainable-artificial-intellig-ence).However, no visible results have yet been revealed.

In the medical field, as a technique for segmenting, detecting,classifying and diagnosing lesions having complex shapes, a techniquefor selectively applying a plurality of segmentation algorithms isdisclosed in International Publication No. WO2018/015414 entitled“Method and System for Artificial Intelligence Based Medical ImageSegmentation.”

In the related art document, a technique of comparing pre-trainedsegmentation algorithms and selecting at least one of the pre-trainedsegmentation algorithms is applied to the acquisition of a final resultof image segmentation.

However, descriptive information (explanation) about the criteria forthe selective application of the segmentation algorithms cannot bederived from the related art document, and thus a problem arises in thatit is difficult to increase a clinician's confidence in the clinicalusefulness of this segmentation technique.

Moreover, Korean Patent No. 10-1938992 entitled “CAD System and Methodfor Generating Description of Reason for Diagnosis” introduced atechnology that generated feature vectors by concatenating featureinformation extracted based on a DNN in order to derive groundinformation for the diagnosis of a lesion. However, in Korean Patent No.10-1938992, an artificial neural network derives feature information byitself, and no verification is made as to whether or not the extractedfeature information is clinically useful information. Accordingly, thereis little evidence that humans can recognize the above information as adescription of the diagnosis result of artificial neural networks.

A similar problem is still present in a medical image diagnosis processin that it is difficult to have clinical confidence in a process inwhich an artificial intelligence diagnosis system that operates like ablack box generates a result.

SUMMARY OF THE DISCLOSURE

Recently, efforts have been made to improve the performance of imagesegmentation, object detection, and object classification techniques byapplying deep learning-based artificial intelligence techniques.However, in the case of deep learning-based artificial intelligence, thefact that there is a black box that prevents a user from determiningwhether or not a result provided from an operation accidentally exhibitshigh performance and whether or not a determination process appropriatefor a corresponding task has been performed limits the applicability ofthe deep learning-based artificial intelligence.

In contrast, the use of rule-based training or learning, which is easyto explain, is limited in that better performance cannot be achievedthan deep learning. Accordingly, research into deep learning-basedartificial intelligence that can provide descriptive information(explanation) while having improved performance is being activelyconducted. In the practical application of image processing using anartificial neural network, descriptive information about the basis ofdiagnosis and classification is required particularly in the medicalimaging field. However, descriptive information cannot be derived fromthe related art.

Even in the above-described related art document (InternationalPublication No. WO2018/015414), it is not possible to derive descriptiveinformation (explanation) on factors that affect the improvement offinal segmentation performance, and there is no way to verify thatclinically significant feedback has been actually and appropriatelyapplied to the deep learning system even when a clinician provides theclinically significant feedback.

An object of the present invention is to provide evaluation scores,including confidence and accuracy scores, for a plurality of medicalimage diagnosis algorithms in a process in which a user diagnoses amedical image, thereby improving the accuracy of a medical imagediagnosis result obtained by the user.

An object of the present invention is to provide recommended informationas descriptive information in a process in which a user derives a finaldiagnosis result by using artificial intelligence medical imagediagnosis algorithms, and to allow the user to provide information aboutthe clinical usefulness of the medical image diagnosis algorithms asquantified information.

An object of the present invention is to provide the optimized contentof artificial intelligence medical image diagnosis results for eachreal-time image frame(video frame) of an endoscopic image.

An object of the present invention is to provide the optimized contentof a plurality of artificial intelligence medical image diagnosisresults for each real-time image frame.

An object of the present invention is to generate and provide anoptimized combination of a plurality of artificial intelligence medicalimage diagnosis results as display information for each real-time imageframe.

An object of the present invention is to provide an optimizedcombination of a plurality of artificial intelligence medical imagediagnosis results capable of efficiently displaying diagnosis resultsthat are likely to be diagnosed, are likely to be overlooked, or have ahigh level of risk in a current image frame.

An object of the present invention is to provide a user interface anddiagnosis computing system that automatically detect and presentdiagnosis results that are likely to be diagnosed, are likely to beoverlooked, or have a high level of risk in a current image frame, sothat medical staff can check and review the diagnosis results in realtime during an endoscopy.

According to an aspect of the present invention, there is provided amedical image diagnosis assistance apparatus including a computingsystem, wherein the computing system includes: a reception interfacemodule; a memory or database; and a processor. The reception interfacemodule is configured to receive a medical image, and the memory ordatabase is configured to store a plurality of medical image diagnosisalgorithms each having a medical image diagnosis function.

The processor is configured to extract diagnosis requirements for themedical image by analyzing each image frame(video frame) of the medicalimage, is further configured to select a plurality of diagnosisapplication algorithms to be applied to the diagnosis of the image framefrom among the plurality of medical image diagnosis algorithms based onthe diagnosis requirements, and is further configured to generatedisplay information including diagnosis results for the image frame byapplying the plurality of selected diagnosis application algorithms tothe image frame.

The processor may be further configured to extract context-baseddiagnosis requirements corresponding to characteristics of the imageframe of the medical image by analyzing the image frame of the medicalimage. The processor may be further configured to select a plurality ofdiagnosis application algorithms to be applied to the diagnosis of theimage frame based on the context-based diagnosis requirements.

The processor may be further configured to select a combination of theplurality of diagnosis application algorithms based on the context-baseddiagnosis requirements. The processor may be further configured togenerate display information including diagnosis results for the imageframe by applying the combination of the plurality of diagnosisapplication algorithms to the image frame.

The combination of the plurality of diagnosis application algorithms mayinclude: a first diagnosis application algorithm configured to bepreferentially recommended for the image frame based on thecontext-based diagnosis requirements; and a second diagnosis applicationalgorithm configured to be recommended based on a supplemental diagnosisrequirement derived from the context substrate diagnosis requirementsbased on a characteristic of the first diagnosis application algorithm.

The context-based diagnosis requirements may include one or more of abody part of the human body included in the endoscopic image frame, anorgan of the human body, a relative position indicated by the endoscopicimage frame in the organ of the human body, the probabilities ofoccurrence of lesions related to the endoscopic image frame, the levelsof risk of the lesions related to the endoscopic image frame, the levelsof difficulty of identification of the lesions related to the endoscopicimage frame, and the types of target lesions.

The display information may include the image frame, the diagnosisresults selectively overlaid on the image frame, information about thediagnosis application algorithms having generated the diagnosis results,and evaluation scores for the diagnosis application algorithms.

The processor may be further configured to store the display informationin the database with the display information associated with the imageframe.

The processor may be further configured to generate external storagedata in which the display information is associated with the imageframe, and to transmit the external storage data to an external databasethrough a transmission interface module so that the external storagedata is stored in the external database.

In this case, the plurality of medical image diagnosis algorithms may beartificial intelligence algorithms each using an artificial neuralnetwork, and the processor may generate evaluation scores for therespective diagnosis requirements as descriptive information about eachof the plurality of medical image diagnosis algorithms.

According to another aspect of the present invention, there is provideda medical image diagnosis assistance method that is executed by aprocessor inside a computing system for assisting the diagnosis of amedical image and is also executed based on program instructions loadedinto the processor.

The medical image diagnosis assistance method includes: receiving, by areception interface module, a medical image; extracting, by theprocessor, diagnosis requirements for the medical image by analyzingeach endoscopic image frame of a medical image; selecting, by theprocessor, a plurality of diagnosis application algorithms to be appliedto the diagnosis of the image frame from among a plurality of medicalimage diagnosis algorithms stored in a memory or database inside thecomputing system and each having a medical image diagnosis functionbased on the diagnosis requirements; and generating, by the processor,display information including diagnosis results for the image frame byapplying the plurality of selected diagnosis application algorithms tothe image frame.

BRIEF DESCRIPTION OF THE DRAWINGS

The above and other objects, features and advantages of the presentinvention will be more clearly understood from the following detaileddescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is a conceptual diagram showing an overall workflow including amedical image diagnosis assistance apparatus according to an embodimentof the present invention;

FIG. 2 is an operational flowchart showing a medical image diagnosisassistance method that is performed by the medical image diagnosisassistance apparatus according to an embodiment of the presentinvention;

FIG. 3 is a conceptual diagram showing an overall workflow including amedical image diagnosis assistance apparatus according to an embodimentof the present invention;

FIG. 4 is a diagram showing the medical image diagnosis assistanceapparatus of FIG. 3 in detail;

FIG. 5 is a conceptual diagram showing a workflow including a medicalimage diagnosis assistance apparatus according to an embodiment of thepresent invention; and

FIG. 6 is a conceptual diagram showing a workflow including a medicalimage diagnosis assistance apparatus according to an embodiment of thepresent invention.

DETAILED DESCRIPTION OF THE DISCLOSURE

Embodiments of the present invention will be described in detail belowwith reference to the accompanying drawings. In the followingdescription, when it is determined that a detailed description of aknown component or function may unnecessarily make the gist of thepresent invention obscure, it will be omitted.

When recently rapidly developed deep learning/CNN-based artificialneural network technology is applied to the imaging field, it may beused to identify visual elements that are difficult to identify with theunaided human eye. The application of this technology is expected toexpand to various fields such as security, medical imaging, andnon-destructive inspection.

For example, in the medical imaging field, there are cases where cancertissue is not immediately diagnosed as cancer during a biopsy but isdiagnosed as cancer after being tracked and monitored from apathological point of view. Although it is difficult for the human eyeto confirm whether or not corresponding cells are cancer in a medicalimage, there is an expectation that the artificial neural networktechnology can provide a more accurate prediction than the human eye.

However, although the artificial neural network technology can yieldbetter prediction/classification/diagnosis results than the human eye insome studies, there is a lack of descriptive information aboutprediction/classification/diagnosis results acquired through theapplication of the artificial neural network technology, and thus aproblem arises in that it is difficult to accept and adopt the aboveresults in the medical field.

The present invention has been conceived from the intention to improvethe performance of the classifying/predicting objects in an image, whichare difficult to classify with the unaided human eye, through theapplication of the artificial neural network technology. Furthermore,even in order to improve the classification/prediction performance ofthe artificial neural network technology, it is significantly importantto acquire descriptive information about the internal operation thatreaches the generation of a final diagnosis result based on theclassification/prediction processes of the artificial neural networktechnology.

The present invention may present the performance indicators andclinical usefulness of a plurality of medical image diagnosis algorithmsbased on artificial neural networks as quantified indicators. As aresult, it is possible to provide descriptive information about aprocess of acquiring a final diagnosis result based on theclassification/prediction processes of the artificial neural network,and it is also possible to provide a reference for the determination ofwhether or not a human user can adopt theclassification/prediction/diagnosis results of an artificial neuralnetwork.

When the artificial neural networks of the related art are applied tothe diagnosis of medical images, they are overfitted only for giventasks, so that statistical accuracy is high but accuracy is low in someclinically important diagnostic points. Many neural networks of therelated art are in such a situation, and thus there occur frequent caseswhere it is difficult for clinicians to have confidence in the diagnosisresults for medical images to which the artificial neural networks areapplied. This risk is more obvious in that IBM's Watson Solution, awell-known artificial neural network, exhibits a problem in that it isoverfitted for patient race information included in learned data andthus it is significantly low in accuracy in the case of the dataset ofnew race patients.

Therefore, it is significantly important to provide a route throughwhich quantified indicators regarding whether or not clinicians willaccept these diagnosis results can be provided and clinicians canprovide direct feedback on the generation of the quantified indicatorswhile maximally utilizing the excellent analytical/diagnostic potentialof the artificial neural networks.

A medical image diagnosis assistance apparatus and method according toembodiments of the present invention will be described in detail belowwith reference to FIGS. 1 and 2 .

FIG. 1 is a conceptual diagram showing an overall workflow including themedical image diagnosis assistance apparatus according to the embodimentof the present invention. Although the partial sequence (steps S110,S120, and S130) of FIG. 1 may belong to the related art, it isincorporated into the present invention as part of the configuration ofthe present invention.

FIG. 2 is an operational flowchart showing the medical image diagnosisassistance method that is performed by the medical image diagnosisassistance apparatus according to the embodiment of the presentinvention.

Referring to FIG. 1 , a medical image acquired by a diagnostic imagingapparatus (a modality) at step S110 is transferred to a diagnosiscomputing terminal of medical staff and is transferred to a computingsystem 100 according to the present invention at step S140.

The diagnostic imaging apparatus refers to a modality capable ofsearching for the traces of a lesion within an anatomical structure ofthe human body or an organ of the human body, such as a CT scanner, anMRI scanner or an ultrasonic diagnosis apparatus, and is not limited toa specific type of apparatus.

The medical image diagnosis assistance apparatus according to thepresent invention is configured to include the computing system 100, andthe medical image diagnosis assistance method according to the presentinvention is performed by the computing system 100. The computing system100 includes a memory or database, and also includes a processor. Themedical image diagnosis assistance method according to the presentinvention may be implemented in a form in which program instructionsstored in the memory or database are invoked by a processor, loaded intothe processor, and executed by the processor.

Referring to FIGS. 1 and 2 , the computing system 100 selects aplurality of diagnosis application algorithms to be applied to thediagnosis of an input medical image at step S220 by analyzing the inputmedical image at step S210.

The computing system 100 generates I-scores 170 for the plurality ofrespective diagnosis application algorithms as evaluation scores atsteps S160 and S240. The computing system 100 may generate a pluralityof recommended diagnosis results for one examination or at least onemedical image at one time.

The computing system 100 may store a plurality of medical imagediagnosis algorithms each having a medical image diagnosis function inthe memory or database. The processor of the computing system 100extracts a diagnosis requirement for the medical image by analyzing themedical image at step S210. The processor selects a plurality ofdiagnosis application algorithms to be applied to the diagnosis of themedical image from among the plurality of medical image diagnosisalgorithms based on the diagnosis requirement at step S220.

When the plurality of selected diagnosis application algorithms isapplied to the medical image and a plurality of diagnosis results forthe medical image is generated, the processor of the computing system100 stores the plurality of diagnosis results for the medical image inthe memory or database inside the computing system 100 with theplurality of diagnosis results for the medical image associated with theplurality of respective diagnosis application algorithms. In this case,for ease of description, for example, it is assumed that when theplurality of diagnosis application algorithms includes a first diagnosisalgorithm and a second diagnosis algorithm, a first diagnosis resultbased on the first diagnosis algorithm and a second diagnosis resultbased on the second diagnosis algorithm are obtained.

The processor of the computing system 100 generates I-scores 170 for theplurality of respective diagnosis application algorithms as evaluationscores at steps S160 and S240.

At step S130, diagnosis results are generated using results to whichartificial intelligence algorithms are applied by the diagnosiscomputing terminal of the medical staff at step S120. In this case, thecomments of the medical staff may be added at step S130 of generatingdiagnosis results.

In the medical image diagnosis assistance apparatus according to thepresent invention, the I-scores 170, i.e., evaluation scores, aretransferred from the computing system 100 to the diagnosis computingterminal of the medical staff at step S180. Final diagnosis texts may begenerated by incorporating the I-scores 170, i.e., evaluation scores,into the generation of the diagnosis results at step S130. According toan embodiment of the present invention, the computing system 100 maygenerate diagnosis texts together with the I-scores 170, i.e.,evaluation scores, and transfer them to the computing system of themedical staff at step S180. In this case, the diagnosis texts generatedby the computing system 100 may be written using the diagnosis resultsbased on diagnosis application algorithms having higher I-scores 170,i.e., higher evaluation scores.

The computing system 100 may provide a user interface configured todisplay a plurality of recommended diagnosis results at one time, togenerate the plurality of recommended diagnosis results withoutrequiring the additional operation of a user, and to check/display theplurality of recommended diagnosis results. The processor of thecomputing system 100 may generate display information includingevaluation scores for the plurality of respective diagnosis applicationalgorithms and the plurality of diagnosis results, and may provide auser with a menu configured to allow a user to select one or more of theplurality of diagnosis application algorithms.

The computing system 100 provides a system or interface configured todisplay the comparisons between suspected body parts for one examinationor at least one image or to present diagnosis texts in response to asearch for a plurality of recommended diagnosis results. The processorof the computing system 100 may display a first diagnosis result and asecond diagnosis result for suspected lesion locations within themedical image so that they can be compared with each other, and maygenerate a first diagnosis text based on the first diagnosis result forthe suspected lesion locations within the medical image and a seconddiagnosis text based on the second diagnosis result therefor. In otherwords, when different diagnosis results are obtained by applyingdifferent diagnosis application algorithms to the same medical image,this plurality of diagnosis results is displayed such that they can becompared with each other, and the user may select any one of first andsecond diagnosis results and generate it as a final diagnosis result. Inthis case, a user interface configured to determine whether or not theuser will accept a diagnosis result for each of the suspected lesionlocations presented by the first and second diagnosis results may beprovided, and the accuracies of the first and second diagnosis resultsmay be compared with each other for each lesion diagnosis result.Information about whether or not the user finally has accepted each ofthe plurality of automatic diagnosis results for each lesion diagnosisresult may be transferred back to the computing system 100 as feedbackinformation, and may be utilized as sub-information that constitutespart of an evaluation score.

The computing system 100 may provide a user interface configured tocompare and display base images or diagnosis texts corresponding to theplurality of recommended diagnosis results. The processor of thecomputing system 100 generates first diagnosis base image informationincluding information about suspected lesion locations within themedical image associated with the first diagnosis result and seconddiagnosis base image information including information about suspectedlesion locations within the medical image associated with the seconddiagnosis result, and may generate a first diagnosis text based on thefirst diagnosis base image information and the first diagnosis resultand a second diagnosis text based on the second diagnosis base imageinformation and the second diagnosis result. When the first diagnosisresult and the second diagnosis result are different for the samesuspected lesion location within the medical image, the user may invokeand compare the first diagnosis base image information of the firstdiagnosis result and the second diagnosis base image information of thesecond diagnosis result. Since the originals of the base medical imagesare the same and the regions diagnosed as lesions by the respectivediagnosis application algorithms are different from each other, theregions of the originals of the medical images referred to by therespective diagnosis application algorithms as bases for the diagnosesof the lesions may be different from each other in this case. Forexample, when the diagnosis application algorithms perform a functionsuch as object detection, diagnosis base image information may berepresented as a box including a specific object or information aboutthe contour lines of a specific object. Furthermore, information aboutthe probabilities at which the diagnosis application algorithms havedetected the corresponding objects may be included in and generated asdiagnosis base image information.

The computing system 100 may provide a user interface configured toallow recommended diagnosis results to be selected using I-scores 170,i.e., internally calculated evaluation scores, and to allow aradiologist to evaluate/check diagnostic confidence in correspondingrecommended diagnoses (e.g., recommended diagnosis algorithms consistentwith the diagnosis results of the radiologist) because the evaluationscores are also displayed. The processor of the computing system 100 mayselect the first and second diagnosis results from among the pluralityof diagnosis results as recommended diagnosis results based on theevaluation scores. The processor may generate display informationincluding the evaluation score for the first diagnosis algorithm, thefirst diagnosis result, the evaluation score for the second diagnosisalgorithm, and the second diagnosis result.

The computing system 100 may generate an evaluation score based on theconfidence score of a corresponding diagnosis algorithm, the accuracyscore of the diagnosis algorithm, and the evaluation confidence score ofa radiologist who provides feedback. The processor may generate theconfidence score of each of the plurality of medical image diagnosisalgorithms, the accuracy score of the medical image diagnosis algorithm,and the evaluation confidence score of the medical image diagnosisalgorithm by the user as sub-evaluation items based on a correspondingone of the plurality of diagnosis results and feedback on the diagnosisresult, and may generate an evaluation score based on the sub-evaluationitems.

For example, the criteria for the generation of the evaluation score maybe implemented as follows:I-score=a×(the confidence score of an artificial intelligencealgorithm)+b×(the accuracy score of the artificial intelligencealgorithm)+c×(the evaluation confidence score of the artificialintelligence algorithm by a radiologist)  (1)

The confidence score of the algorithm may be given to the algorithm bythe radiologist. In other words, when it is determined that the firstdiagnosis result is more accurate than the second diagnosis result, ahigher confidence score may be given to the first diagnosis result.

The accuracy score of the algorithm may be determined based on theextent to which the radiologist accepts the diagnosis result of thealgorithm without a separate score giving process. For example, in thecase where when the first diagnosis result presents ten suspected lesionlocations, the radiologist approves nine suspected lesion locations, theaccuracy score may be given as 90/100.

Another embodiment in which the accuracy score of the algorithm is givenmay be a case where an accurate result is revealed through a biopsy orthe like. In this case, the accuracy of the diagnosis result of thediagnosis algorithm may be revealed in comparison with the accurateresult obtained through the biopsy. When the user inputs the accurateresult, obtained through the biopsy, to the computing system 100, thecomputing system 100 may calculate the accuracy score of the diagnosisalgorithm by comparing the diagnosis result with the accurate resultobtained through the biopsy (a reference).

The evaluation confidence score of the radiologist may be provided as aconfidence score for the evaluation of the radiologist. In other words,when the radiologist is an expert having a loner experience in acorresponding clinical field, a higher evaluation confidence score maybe given accordingly. The evaluation confidence score may be calculatedby taking into consideration the years of experience of the radiologist,the specialty of the radiologist, whether or not the radiologist is amedical specialist, and the experience in the corresponding clinicalfield.

The computing system 100 may update evaluation score calculationcriteria according to a predetermined internal schedule whilecontinuously learning the evaluation score calculation criteria by meansof an internal artificial intelligence algorithm. The processor mayassign weights to the confidence scores of the plurality of respectivemedical image diagnosis algorithms, the accuracy scores of the pluralityof respective medical image diagnosis algorithms, and the evaluationconfidence scores of the plurality of respective medical image diagnosisalgorithms by the user, which are sub-evaluation items, and may updatethe weights of the sub-evaluation items so that the weights of thesub-evaluation items can be adjusted according to a target requirementbased on the plurality of diagnosis results and feedback on theplurality of diagnosis results by the user.

An example of the target requirement may be a case where adjustment isperformed such that there is a correlation between the confidence of theuser in the algorithms and the accuracy of the algorithms. For example,first and second diagnosis algorithms having the same accuracy score mayhave different confidence scores that are given by a radiologist. Inthis case, when confidence scores are different from each other whileexhibiting a certain tendency after the removal of the general errors ofthe evaluation of the radiologist, it can be recognized that theconfidence of the radiologist in the first diagnosis algorithm isdifferent from the confidence of the radiologist in the second diagnosisalgorithm. For example, in the case where the first and second diagnosisalgorithms generate accurate diagnosis results at nine of a total of tensuspected lesion locations, resulting in an accuracy score of 90/100 butonly the first diagnosis algorithm accurately identifies a severe lesionand the second diagnosis algorithm does not identify the lesion, theconfidence of the radiologist in the first diagnosis algorithm may bedifferent from the confidence of the radiologist in the second diagnosisalgorithm. A means for adjusting the correlation between the accuracyand the confidence may be a means for adjusting the weights of therespective sub-evaluation items or subdividing criteria for theselection of target lesions related to the determination of accuracy. Inthis case, there may be used a method that classifies lesions accordingto criteria such as the hardness/severity of an identified lesion, theposition of the lesion from the center of a medical image, and thedifficulty of identifying the lesion (the difficulty is high in a regionwhere bones, organs, and blood vessels are mixed in a complicated form)and assigns different weights to the diagnosis accuracies of lesions inrespective regions.

The computing system 100 may include a function of automaticallyallocating a plurality of artificial intelligence algorithms that areapplicable depending on an image. To determine a plurality of artificialintelligence algorithms applicable to an image, the computing system 100may classify one examination or at least one image by means of aseparate image classification artificial intelligence algorithm inside arecommendation diagnosis system, and may then apply a plurality ofartificial intelligence algorithms.

The processor may extract an image segmentation result for the medicalimage, a clinical requirement for the medical image, and a diagnosisrequirement for the medical image based on information about theexaminee of the medical image. The processor may select a plurality ofdiagnosis application algorithms to be applied to the diagnosis of themedical image based on at least one of suitability for the diagnosisrequirement and the evaluation scores for the plurality of respectivemedical image diagnosis algorithms. In other words, information about arequest for the acquisition of the medical image, the details of asuspected disease accompanying the request, the department of theclinician, and a body part or organ photographed in the medical image,which is transferred from the clinician to the radiologist, may be takeninto consideration as the clinical requirement. Furthermore, when eachof the diagnosis application algorithms is selected, information aboutthe purpose of diagnosis and the object of diagnosis defined by theartificial intelligence algorithm may be taken into consideration, andalso the gender and age of the examinee of the medical image and theseverity of a disease may be taken into consideration.

The computing system 100 may subdivide the recommended items for thesame diagnosis purpose (the same diagnosis requirement) for the samebody part while associating an image analysis function and an evaluationscore generation function with each other. For example, there may beprovided a system capable of recommending an algorithm suitable for thediagnosis of the center of a particular organ, an algorithm suitable forthe diagnosis of the periphery of a particular organ, an algorithmsuitable for a region where bones, organs, and blood vessels are mixedin a complicated form, etc. in a subdivided manner. This function may beimplemented via a separate artificial intelligence algorithm.

The processor may generate detailed evaluation items for each diagnosisrequirement, including information about the type of organ, the locationof a lesion, and the relative positions of the organ and the lesion foreach diagnosis requirement, based on image segmentation and processingresults for the medical image, and may generate evaluation scores forthe plurality of respective medical image diagnosis algorithms withrespect to each of the detailed evaluation items for each diagnosisrequirement.

The computing system 100 may subdivide criteria items for the generationof evaluation scores based on image diagnosis results. For example, analgorithm with high overall accuracy and low confidence may exhibit highoverall accuracy but may exhibit low accuracy for a specific item. Inthis case, the computing system 100 may specifically select diagnosisitems that highly affect confidence. In other words, the computingsystem 100 may refine and subdivide the criteria items for thegeneration of evaluation scores by using the image diagnosis results.

The processor may generate a confidence score for each of the pluralityof medical image diagnosis algorithms, an accuracy score for the medicalimage diagnosis algorithm, and an evaluation confidence score for themedical image diagnosis algorithm by the user as sub-evaluation itemsbased on a corresponding one of the plurality of diagnosis results andfeedback on the diagnosis result by the user. The processor may selecteach of the plurality of diagnosis application algorithms based on thecorrelation between each detailed evaluation item for the diagnosisrequirement and a corresponding one of the sub-evaluation items.

In an embodiment of the present invention, the plurality of medicalimage diagnosis algorithms may be medical image diagnosis algorithmsusing artificial neural networks. In this case, the evaluation score andthe sub-evaluation items may be generated as descriptive information foreach diagnosis algorithm, and the computing system 100 may feed theevaluation score and the sub-evaluation items back to the creator of thediagnosis algorithm so that the information can be used to improve thediagnosis algorithm. In this case, when each of the artificial neuralnetworks is an artificial neural network using a relevance score and aconfidence level, which is being studied recently, a statisticalanalysis is performed with the evaluation score and the sub-evaluationitems associated with the relevance score or confidence level of theartificial neural network, and thus the evaluation score and thesub-evaluation items may affect the improvement of the diagnosisalgorithm.

In an embodiment of the present invention, after the applicablealgorithms have been selected by the computing system 100, informationabout the selected diagnosis application algorithms may be transferredto the diagnosis computing terminal of the medical staff at step S145,and artificial intelligence algorithms (the diagnosis applicationalgorithms) may be actually applied to the medical image in thediagnosis computing terminal of the medical staff at step S120.

In this case, a plurality of diagnosis results obtained by actuallyapplying the plurality of diagnosis application algorithms istransferred to the computing system 100 at step S150. The computingsystem 100 may store the plurality of diagnosis results in the memory ordatabase inside the computing system 100 with the plurality of diagnosisresults associated with the plurality of diagnosis applicationalgorithms. In this case, the feedback indicators input for theplurality of diagnosis results or the plurality of diagnosis applicationalgorithms via the diagnosis computing terminal of the medical staff bythe medical staff may be also fed back to the computing system at stepS150. The feedback indicators may be stored in the memory or databaseinside the computing system 100 in association with the evaluationtargets, i.e., the plurality of diagnosis results or the plurality ofdiagnosis application algorithms.

This embodiment of the present invention is designed to provideadvantages obtainable by the present invention while minimizing thedeformation of the medical image diagnosis sequence S110, S120 and S130of the related art as much as possible.

In another embodiment of the present invention, the computing system 100may perform the process of selecting a plurality of diagnosisapplication algorithms and generating a plurality of diagnosis resultsby applying the plurality of diagnosis application algorithms to amedical image by itself. In this case, the computing system 100 maytransfer not only information about the selected diagnosis applicationalgorithms but also the plurality of diagnosis results based on thediagnosis application algorithms to the diagnosis computing terminal ofthe medical staff at step S145, and the results obtained by applyingartificial intelligence algorithms (the diagnosis applicationalgorithms) to the medical image may be displayed on the diagnosiscomputing terminal of the medical staff at step S120.

In this case, an embodiment of the present invention may provideadvantages obtainable by present invention even when the computing powerof the diagnosis computing terminal of the medical staff is not high,e.g., the diagnosis computing terminal of the medical staff is a mobiledevice or an old-fashioned computing system. In this case, in anembodiment of the present invention, an agent that applies theartificial intelligence algorithms to the medical image is the computingsystem 100, the computing system 100 functions as a type of server, andthe diagnosis computing terminal of the medical staff may operate basedon a thin-client concept. In this case, in an embodiment of the presentinvention, the feedback indicators input for the plurality of diagnosisresults or the plurality of diagnosis application algorithms via thediagnosis computing terminal of the medical staff by the medical staffmay be fed back to the computing system at step S150. The feedbackindicators may be stored in the memory or database inside the computingsystem 100 in association with the evaluation targets, i.e., theplurality of diagnosis results or the plurality of diagnosis applicationalgorithms.

As described above, in an embodiment of the present invention, step S230of applying the selected algorithms may be performed in the diagnosissystem of the clinician at step S120, and a plurality of diagnosisresults may be transferred to the computing system 100 at step S150. Inanother embodiment of the present invention, overall step S230 ofapplying the selected algorithms may be performed within the computingsystem 100 and then the results of the application may be displayed onthe diagnosis system of the clinician at steps S145 and S120.

FIG. 3 is a conceptual diagram showing an overall workflow including amedical image diagnosis assistance apparatus according to an embodimentof the present invention.

A real-time image acquisition interface module 330 acquires a real-timeendoscopic image 350 from endoscopy equipment 320. The real-time imageacquisition interface module 330 transmits the real-time endoscopicimage 350 to a user system 340 and a diagnosis system 310. The diagnosissystem 310 includes at least two artificial intelligence algorithms 312and 314, and generates display information 360 including diagnosisinformation by applying the at least two artificial intelligencealgorithms 312 and 314 to the real-time endoscopic image 350. Thediagnosis system 310 transfers the display information 360 to the usersystem 340, and the user system 340 may overlay the display information360 on the real-time endoscopic image 350 or display the real-timeendoscopic image 350 and the display information 360 together.

Although the embodiment in which the diagnosis system 310 and the usersystem 340 are separated from each other is illustrated in FIG. 3 forease of illustration, it will be apparent to those skilled in the artthat the diagnosis system 310 and the user system 340 may be implementedin a single system according to another embodiment of the presentinvention.

FIG. 4 is a diagram showing the medical image diagnosis assistanceapparatus of FIG. 3 in detail.

The real-time endoscopic image 350 may be divided into individual imageframes. In this case, each of the endoscopic image frames 450 may bereceived or input by a reception interface module 430.

The diagnosis computing system 410 includes the reception interfacemodule 430, a processor 420, and a transmission interface module 440.The processor 420 includes sub-modules the functions of which areinternally implemented by hardware or software. The processor 420 mayinclude a first sub-module 422 configured to extract context-baseddiagnosis requirements, a second sub-module 424 configured to selectartificial intelligence analysis results to be displayed from amongdiagnosis results generated by applying artificial intelligencediagnosis algorithms to the endoscopic image frame 450, and a thirdsub-module 426 configured to generate the display information 360 to bedisplayed on the screen of the user system 340.

The plurality of artificial intelligence diagnosis algorithms is storedin memory or database (not shown) inside the diagnosis computing system410, is applied to the endoscopic image frame 450 under the control ofthe processor 420, and generates diagnosis results for the endoscopicimage frame 450.

Although a case where the plurality of artificial intelligence diagnosisalgorithms is stored in the memory or database (not shown) inside thediagnosis computing system 410 and run under the control of theprocessor 420 is described in the embodiment of FIG. 4 , the pluralityof artificial intelligence diagnosis algorithms may be stored in amemory or database (not shown) outside the diagnosis computing system410 according to another embodiment of the present invention. When theplurality of artificial intelligence diagnosis algorithms is stored inthe memory or database (not shown) outside the diagnosis computingsystem 410, the processor 420 may control the memory or database (notshown) outside the diagnosis computing system 410 via the transmissioninterface module 440 so that the plurality of artificial intelligencediagnosis algorithms is applied to the endoscopic image frame 450 anddiagnosis results for the endoscopic image frame 450 are generated. Inthis case, the generated diagnosis results may be transferred to thediagnosis computing system 410 through the reception interface module430, and the processor 420 may generate the display information 360based on the diagnosis results.

The processor 420 extracts diagnosis requirements for the endoscopicimage frame 450 by analyzing the endoscopic image frame 450, which is animage frame (video frame) of a medical image. The processor 420 selectsa plurality of diagnosis application algorithms to be applied to thediagnosis of the endoscopic image frame 450 from among the plurality ofmedical image diagnosis algorithms based on the diagnosis requirements,and the processor 420 generates the display information 360 includingdiagnosis results for the endoscopic image frame 450 by applying theplurality of diagnosis application algorithms to the endoscopic imageframe 450. This process is performed on each of the endoscopic imageframes 450 by the processor 420.

The processor 420 may extract context-based diagnosis requirementscorresponding to the characteristics of the endoscopic image frame 450by analyzing the endoscopic image frame 450. The processor 420 mayselect a plurality of diagnosis application algorithms to be applied tothe diagnosis of the endoscopic image frame 450 based on thecontext-based diagnosis requirements.

The processor 420 may select a combination of a plurality of diagnosisapplication algorithms based on the context-based diagnosisrequirements. The processor 420 may generate the display information 360including diagnosis results for the endoscopic image frame 450 byapplying the plurality of diagnosis application algorithms to theendoscopic image frame 450.

The combination of a plurality of diagnosis application algorithms mayinclude a first diagnosis application algorithm configured to bepreferentially recommended for the endoscopic image frame 450 based oncontext-based diagnosis requirements, and a second diagnosis applicationalgorithm configured to be recommended based on a supplemental diagnosisrequirement derived from the context substrate diagnosis requirementsbased on a characteristic of the first diagnosis application algorithm.

The context-based diagnosis requirements may include one or more of abody part of the human body included in the endoscopic image frame 450,an organ of the human body, a relative position indicated by theendoscopic image frame 450 in the organ of the human body, theprobabilities of occurrence of lesions related to the endoscopic imageframe 450, the levels of risk of the lesions related to the endoscopicimage frame 450, the levels of difficulty of identification of thelesions related to the endoscopic image frame 450, and the types oftarget lesions. When an organ to which the endoscopic image frame 450 isdirected is specified, for example, when the endoscopic image frame 450is related to a colonoscopy image, information about whether the imagedisplayed in the current image frame is the beginning, middle, or end ofthe colonoscopy image can be identified along with a relative positionin the colon (the inlet, middle, and end of the organ). Accordingly, thecontext-based diagnosis requirements may be extracted based on the typesof lesions/diseases that are likely to occur at the identified positionand region, the types of lesions/diseases that are likely to beoverlooked by medical staff because they are difficult to identify withthe naked eye, diagnosis information about lesions/diseases that are noteasy to visually identify within the current image frame, and the typesof lesions/diseases requiring attention due to their high risk/lethalityduring a diagnosis among the lesions/diseases that may occur atlocations within the organ of the human body to which the current imageframe is directed. In this case, the context-based diagnosisrequirements may also include information about the types of targetlesions/diseases that need to be first considered in relation to thecurrent image frame based on the information described above.

The display information 360 may include the endoscopic image frame 450,the diagnosis results selectively overlaid on the endoscopic image frame450, information about the diagnosis application algorithms havinggenerated the diagnosis results, and evaluation scores for the diagnosisapplication algorithms. The above-described process of calculatingevaluation scores in the embodiments of FIGS. 1 and 2 may be used as theprocess of calculating the evaluation scores for the diagnosisapplication algorithms.

Although priorities may be allocated to the artificial intelligencediagnosis algorithm in descending order of evaluation scores in theapplication of diagnoses, there are some additional factors to be takeninto account.

When a first-priority artificial intelligence algorithm detects a partof the lesions that are likely to occur in connection with thecorresponding endoscopic image and a subsequent-priority artificialintelligence algorithm detects an item that is not detected by the firstpriority algorithm, both the diagnosis results of the first-priorityartificial intelligence algorithm and the diagnosis results of thesubsequent-priority artificial intelligence algorithm may be displayedtogether. Furthermore, there may be provided a menu that allows a userto select a final diagnosis application artificial intelligencealgorithm based on the above-described criteria. In order to help theuser to make a selection, the menu may be displayed together with thediagnosis results of the plurality of AI algorithms and a description ofthe reason for displaying the diagnosis results.

For example, it is assumed that lesions A1 and A2 are known as being thetypes of lesions that are most likely to occur within the current imageframe and a lesion B is known as being less likely to occur than lesionsA1 and A2 and being likely to be overlooked because it is difficult tovisually identify. An artificial intelligence diagnosis algorithm X,which has obtained the highest evaluation score for the lesions A1 andA2, may obtain the highest overall evaluation score and be selected asthe first diagnosis application algorithm that is preferentiallyrecommended. Meanwhile, there may be a case where the first diagnosisapplication algorithm obtains the highest evaluation score for thelesions A1 and A2 but obtains an evaluation score less than a referencevalue for the lesion B. In this case, the lesion B for which the firstdiagnosis application algorithm exhibits the performance less than thereference value may be designated as a supplemental diagnosisrequirement. An artificial intelligence diagnosis algorithm Y thatobtains the highest evaluation score for the lesion B, which is asupplemental diagnosis requirement, may be selected as a seconddiagnosis application algorithm. A combination of the first and seconddiagnosis application algorithms may be selected such that thecombination has high evaluation scores for the reliability and accuracyof the overall diagnostic information, the diagnostic information for aspecific lesion/disease is prevented from being overlooked, and thediagnostic performance for a specific lesion/disease is prevented frombeing poor. Accordingly, logical conditions for the selection of adiagnosis application algorithm may be designed such that an artificialintelligence diagnosis algorithm exhibiting the best performance for thesupplemental diagnosis requirement for which the first diagnosisapplication algorithm is weak, rather than the AI reading algorithmexhibiting high overall evaluation score, is selected as the seconddiagnosis application algorithm.

Although the case where the two diagnosis application algorithms areselected has been described as an example in the above embodiment, anembodiment in which three or more diagnosis application algorithms areselected and applied may also be implemented according to thedescription given herein in the case where the combination of the threeor more diagnosis application algorithms exhibits better performanceaccording to the evaluation scores.

The embodiments of FIGS. 1 to 2 are embodiments in which the diagnosisresults obtained by the application of the artificial intelligencediagnosis algorithms having high internal evaluation scores arepresented and then a user may select the diagnosis results obtained bythe application of artificial intelligence diagnosis algorithms havinghigher evaluation scores. Meanwhile, in the embodiment of FIGS. 3 and 4, there is disclosed a configuration conceived for the purpose ofrapidly displaying diagnosis results for a real-time endoscopic image.Accordingly, in the embodiment of FIGS. 3 to 4 , a combination ofartificial intelligence diagnosis algorithms to be displayed for thecurrent image frame is preferentially selected based on context-baseddiagnosis requirements, the diagnosis results of this combination aregenerated as display information, and the display information togetherwith the image frame is provided to a user.

In this case, the types of lesions/diseases that are likely to occur inthe current image frame, the types of lesions/diseases that are likelyto occur in the current image frame and are also likely to be overlookedby medical staff because they are difficult to visually identify, andthe types of lesions/diseases requiring attention during diagnosis dueto their high risk/lethality among the lesions that may occur in thecurrent image frame may be included in the context-based diagnosisrequirements. Furthermore, the types of target lesions/diseases thatshould not be overlooked in the current image frame based on the typesand characteristics of lesions/diseases, and the priorities of the typesof target lesions/diseases may be included in the context-baseddiagnosis requirements.

When the diagnosis results and display information 360 of the presentinvention are used in a hospital, they are displayed by the user system340 having a user interface capable of displaying auxiliary artificialintelligence diagnosis results after endoscopic data has been receivedand analyzed, and then the diagnosis results may be confirmed, thediagnosis results may be replaced, or the acceptance or rejection of thediagnosis results may be determined based on user input.

The processor 420 may store the display information 360 in the databasewith the display information 360 associated with the endoscopic imageframe 450. In this case, the database may be a database inside thediagnosis computing system 410, and may be stored as medical records fora patient in the future.

The processor 420 may generate external storage data in which thedisplay information 360 and the endoscopic image frame 450 areassociated with each other, and may transmit the external storage datato an external database through the transmission interface module 440 sothat the external storage data can be stored in the external database.In this case, the external database may be a PACS database or a databaseimplemented based on a cloud.

In this case, the plurality of medical image diagnosis algorithms areartificial intelligence algorithms each using an artificial neuralnetwork, and the processor 420 may generate evaluation scores based onthe respective diagnosis requirements/context-based diagnosisrequirements as descriptive information for each of the plurality ofmedical image diagnosis algorithms.

The medical image diagnosis assistance method according to the otherembodiment of the present invention is executed by the processor 420 inthe diagnostic computing system 410 that assists a diagnosis of amedical image, and is executed based on program instructions loaded intothe processor 420.

The medical image diagnosis assistance method includes: the step ofreceiving, by the reception interface module 430, a medical image; step422 of extracting, by the processor 420, diagnosis requirements for themedical image by analyzing each endoscopic image frame 450 of a medicalimage; step 424 of selecting, by the processor 420, a plurality ofdiagnosis application algorithms to be applied to the diagnosis of theendoscopic image frame 450 from among a plurality of medical imagediagnosis algorithms stored in a memory or database in the diagnosiscomputing system 410 and each having a medical image diagnosis functionbased on the diagnosis requirements; and step 426 of generating, by theprocessor 420, display information 360 including diagnosis results forthe endoscopic image frame 450 by applying the plurality of selecteddiagnosis application algorithms to the endoscopic image frame 450.

FIG. 5 is a conceptual diagram showing a workflow including a medicalimage diagnosis assistance apparatus according to an embodiment of thepresent invention.

A diagnosis system 510 may internally include at least two artificialintelligence diagnosis algorithms 512 and 514. Endoscopic image data istransferred from three or more pieces of endoscopy equipment to thediagnosis system 510. The diagnosis system 510 generates diagnosisresults by applying the at least two artificial intelligence diagnosisalgorithms 512 and 514 to each frame of the endoscopic image data. Thediagnosis system 510 generates display information by associating thediagnosis results with the frame of the endoscopic image data. In thiscase, the display information may be generated to include theidentification information of a hospital (hospital A) in which theendoscopic image data is generated. Furthermore, the display informationmay be generated to include the identification information (endoscope 1,endoscope 2, or endoscope 3) given to each piece of endoscope equipmentof each hospital.

FIG. 6 is a conceptual diagram showing a workflow including a medicalimage diagnosis assistance apparatus according to an embodiment of thepresent invention.

The diagnosis system 510 of FIG. 5 transmits the generated displayinformation to a cloud-based database, and the endoscopic image data andthe display information are stored in the cloud-based database, with theendoscopy equipment in which the endoscopic image data was generated andthe hospital in which the endoscopic image data was generated beingidentified. The display information may be generated by associatingdiagnosis information with each frame of the endoscopic image data andthen stored. The diagnosis information generated for each frame of theendoscopic image data may be automatically generated based on evaluationscores and context-based diagnosis requirements, as described in theembodiments of FIGS. 1 to 4 .

When the present invention is applied in a cloud environment, endoscopicimage data and diagnosis results may be received by a user system on ahospital side using equipment connected over a wireless communicationnetwork, and auxiliary artificial intelligence diagnosis results may bedisplayed on the user system.

The display information stored in the cloud database may be provided toa hospital designated by a patient, and the patient may receive his orher endoscopic image data and diagnosis information at a hospital thatis convenient to access and also receive a doctor's interpretation ofdiagnosis results and a follow-up diagnosis at the hospital.

The method of assisting the diagnosis of a medical image according to anembodiment of the present invention may be implemented in the form ofprogram instructions, and may be then recorded in a computer-readablestorage medium. The computer-readable storage medium may include programinstructions, data files, and data structures solely or in combination.Program instructions recorded on the storage medium may have beenspecially designed and configured for the present invention, or may beknown to or available to those who have ordinary knowledge in the fieldof computer software.

Examples of the computer-readable storage medium include all types ofhardware devices specially configured to record and execute programinstructions, such as magnetic media, such as a hard disk, a floppydisk, and magnetic tape, optical media, such as compact disk (CD)-readonly memory (ROM) and a digital versatile disk (DVD), magneto-opticalmedia, such as a floptical disk, ROM, random access memory (RAM), andflash memory. Examples of the program instructions include machine code,such as code created by a compiler, and high-level language codeexecutable by a computer using an interpreter. These hardware devicesmay be configured to operate as one or more software modules in order toperform the operation of the present invention, and the vice versa.

However, the present invention is not limited to the embodiments. Likereference symbols in the drawings designate like components. Thelengths, heights, sizes, widths, etc. introduced in the embodiments anddrawings of the present invention may be exaggerated to help tounderstand.

According to the present invention, a user may compare the plurality ofrecommended diagnosis results of a plurality of artificial intelligencemedical image diagnosis algorithms applicable to a corresponding imagewith his or her diagnosis result in the process of analyzing the medicalimage, thereby increasing the accuracy of the diagnosis result for themedical image by the user.

According to the present invention, a plurality of recommended diagnosisresults for parts that may not be identified by a user may be comparedwith each other and referred to via a plurality of artificialintelligence medical image diagnosis algorithms. The user may verify hisor her diagnosis result through comparison with a plurality of diagnosisresults via the corresponding system, thereby achieving accuratediagnosis and increasing the confidence of diagnosis.

Furthermore, base images for a plurality of recommended diagnosisresults may be referred to in the recommendation diagnosis system, sothat the accuracy/confidence of the diagnosis results of a plurality ofrecommended artificial intelligence medical image diagnosis algorithmssimilar to the diagnosis result of a user may be evaluated and theuser's ability to diagnose a medical image may be improved.

The evaluation of recommended artificial intelligence algorithms by auser may be linked to the charging system of an evaluation system insidethe recommendation diagnosis system.

According to the present invention, evaluation scores for artificialintelligence medical image diagnosis algorithms inside therecommendation diagnosis system may be provided as descriptiveinformation, a user may obtain information about the clinical usefulnessof medical image diagnosis algorithms in the process of generating afinal diagnosis result, and the information about the clinicalusefulness may be fed back to the recommendation diagnosis system of thepresent invention.

The descriptive information that is provided by the present inventionmay, in turn, be beneficially used to improve the diagnosis performanceof medical image diagnosis algorithms based on artificial neuralnetworks.

According to the present invention, there may be provided the optimizedcontent of artificial intelligence medical image diagnosis results foreach real-time image frame of an endoscopic image.

According to the present invention, there may be provided the optimizedcontent of a plurality of artificial intelligence medical imagediagnosis results for each real-time image frame.

According to the present invention, there may be provided an optimizedcombination of a plurality of artificial intelligence medical imagediagnosis results as display information for each real-time image frame.

According to the present invention, there may be provided an optimizedcombination of a plurality of artificial intelligence medical imagediagnosis results capable of efficiently displaying diagnosis resultsthat are likely to be diagnosed, are likely to be overlooked, or have ahigh level of risk in a current image frame.

According to the present invention, there may be provided the userinterface and diagnosis computing system that automatically detect andpresent diagnosis results that are likely to be diagnosed, are likely tobe overlooked, or have a high level of risk in a current image frame, sothat medical staff can check and review the diagnosis results in realtime during an endoscopy.

The present invention was derived from the research conducted as part ofthe Fundamental SW Computing Technology Development Project sponsored bythe Korean Ministry of Science and ICT and the Institute for Informationand Communications Technology Promotion [Project Management Number:2018-0-00861; and Project Name: Development of Intelligent SW Technologyfor Analysis of Medical Data].

Although the present invention has been described with reference tospecific details such as the specific components, and the limitedembodiments and drawings, these are provided merely to help a generalunderstanding of the present invention, and the present invention is notlimited thereto. Furthermore, those having ordinary skill in thetechnical field to which the present invention pertains may make variousmodifications and variations from the above detailed description.

Therefore, the spirit of the present invention should not be definedbased only on the described embodiments, and not only the attachedclaims but also all equivalent to the claims should be construed asfalling within the scope of the spirit of the present invention.

What is claimed is:
 1. A medical image diagnosis assistance apparatusfor assisting a diagnosis of a medical image, the medical imagediagnosis assistance apparatus comprising a computing system, whereinthe computing system comprises: a reception interface module configuredto receive an endoscopic image frame as a medical image; a memory ordatabase configured to store a plurality of medical image diagnosisalgorithms each having a medical image diagnosis function; and aprocessor, wherein the processor is configured to: extract diagnosisrequirements for the medical image by analyzing each image frame of themedical image, extract a relative position indicated by the endoscopicimage frame in a specified organ of a human body indicated by theendoscopic image frame as a part of the diagnosis requirements byanalyzing the endoscopic image frame using an artificial neural network,wherein the relative position in the specified organ includes at leastone of a beginning position, a middle position, or an end position inthe specified organ; select a plurality of diagnosis applicationalgorithms to be applied to a diagnosis of the image frame from amongthe plurality of medical image diagnosis algorithms based on thediagnosis requirements, and generate display information includingdiagnosis results for the image frame by applying the plurality ofselected diagnosis application algorithms to the image frame.
 2. Themedical image diagnosis assistance apparatus of claim 1, wherein theprocessor is further configured to: extract context-based diagnosisrequirements corresponding to characteristics of the image frame of themedical image by analyzing the image frame of the medical image; andselect a plurality of diagnosis application algorithms to be applied tothe diagnosis of the image frame based on the context-based diagnosisrequirements.
 3. The medical image diagnosis assistance apparatus ofclaim 2, wherein the processor is further configured to: select acombination of the plurality of diagnosis application algorithms basedon the context-based diagnosis requirements; and generate displayinformation including diagnosis results for the image frame by applyingthe combination of the plurality of diagnosis application algorithms tothe image frame.
 4. The medical image diagnosis assistance apparatus ofclaim 3, wherein the combination of the plurality of diagnosisapplication algorithms comprises: a first diagnosis applicationalgorithm configured to be preferentially recommended for the imageframe based on the context-based diagnosis requirements; and a seconddiagnosis application algorithm configured to be recommended based on asupplemental diagnosis requirement derived from the context substratediagnosis requirements based on a characteristic of the first diagnosisapplication algorithm.
 5. The medical image diagnosis assistanceapparatus of claim 2, wherein the context-based diagnosis requirementscomprise one or more of a body part of the human body included in theendoscopic image frame, the organ of the human body, the relativeposition indicated by the endoscopic image frame in the organ of thehuman body, probabilities of occurrence of lesions related to theendoscopic image frame, levels of risk of the lesions related to theendoscopic image frame, levels of difficulty of identification of thelesions related to the endoscopic image frame, or types of targetlesions.
 6. The medical image diagnosis assistance apparatus of claim 1,wherein the display information comprises the image frame, the diagnosisresults selectively overlaid on the image frame, information about thediagnosis application algorithms having generated the diagnosis results,and evaluation scores for the diagnosis application algorithms.
 7. Themedical image diagnosis assistance apparatus of claim 1, wherein theprocessor is further configured to store the display information in thedatabase with the display information associated with the image frame.8. The medical image diagnosis assistance apparatus of claim 1, whereinthe processor is further configured to: generate external storage datain which the display information is associated with the image frame; andtransmit the external storage data to an external database through atransmission interface module so that the external storage data isstored in the external database.
 9. A medical image diagnosis assistancemethod, the medical image diagnosis assistance method being executed bya processor inside a computing system for assisting a diagnosis of amedical image, the medical image diagnosis assistance method beingexecuted based on program instructions loaded into the processor, themedical image diagnosis assistance method comprising: receiving, by areception interface module, an endoscopic image frame as a medicalimage; extracting, by the processor, diagnosis requirements for themedical image by analyzing each endoscopic image frame of a medicalimage, wherein the extracting further comprises extracting, by theprocessor, a relative position indicated by the endoscopic image framein awa specified organ of a human body indicated by the endoscopic imageframe as a part of the diagnosis requirements by analyzing theendoscopic image frame using an artificial neural network, wherein therelative position in the specified organ includes at least one of abeginning position, a middle position, or an end position in thespecified organ; selecting, by the processor, a plurality of diagnosisapplication algorithms to be applied to a diagnosis of the image framefrom among a plurality of medical image diagnosis algorithms stored in amemory or database inside the computing system and each having a medicalimage diagnosis function based on the diagnosis requirements; andgenerating, by the processor, display information including diagnosisresults for the image frame by applying the plurality of selecteddiagnosis application algorithms to the image frame.
 10. The medicalimage diagnosis assistance method of claim 9, wherein the extractingcomprises extracting, by the processor, context-based diagnosisrequirements corresponding to characteristics of the image frame of themedical image by analyzing the image frame of the medical image, andwherein the selecting comprises selecting, by the processor, a pluralityof diagnosis application algorithms to be applied to the diagnosis ofthe image frame based on the context-based diagnosis requirements. 11.The medical image diagnosis assistance method of claim 10, wherein theselecting comprises selecting, by the processor, a combination of theplurality of diagnosis application algorithms, including a firstdiagnosis application algorithm configured to be preferentiallyrecommended for the image frame based on the context-based diagnosisrequirements and a second diagnosis application algorithm configured tobe recommended based on a supplemental diagnosis requirement derivedfrom the context substrate diagnosis requirements based on acharacteristic of the first diagnosis application algorithm, based onthe context-based diagnosis requirements, and wherein the generatingcomprises generating, by the processor, display information includingdiagnosis results for the image frame by applying the combination of theplurality of diagnosis application algorithms to the image frame. 12.The medical image diagnosis assistance method of claim 9, furthercomprising: storing, by the processor, the display information in thedatabase with the display information associated with the image frame.13. The medical image diagnosis assistance method of claim 9, furthercomprising: generating, by the processor, external storage data in whichthe display information is associated with the image frame; andtransmitting, by the processor, the external storage data to an externaldatabase through a transmission interface module so that the externalstorage data is stored in the external database.